Methods and systems for digital image security

ABSTRACT

Methods and systems for securing digital imagery are provided. In one respect, embedding, compression, encryption, data hiding, and other imaging processing techniques and systems may be provided for digital image security. In one non-limiting example, a method for producing a compressed and encrypted image is provided. An image may be converted into a binary bit stream, and the bit stream may be decomposed into a plurality of segments. A binary sequence based on a first key may be generated and may be used to generate a code matrix. A distance between the code matrix and the distance may be determined for each of the plurality of segments. Using a combined first and second key, a compressed and encrypted image may be obtained.

This application claims priority to provisional patent application Ser.No. 60/745,729 filed Apr. 26, 2006. The entire text of this disclosure,including figures, is incorporated by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to image processing. Moreparticularly, the present disclosure relates to methods for encryptingdigital images.

2. Description of Related Art

The use of digital documents, medical images, and satellite images hasbeen increasing exponentially along with the interest in imagecompression algorithms. One of the most important problems in variousapplications is the data storage and transmission which in manysituations resolved by the compression techniques. Compression isdefined as the reduction in size (in bytes) of a data/image withacceptable distortions in its quality. There are various compressionstechniques proposed that compress the binary data like Huffman coding,Run length coding, etc. The conventional lossless compression algorithmsare ineffective in compressing data with low redundancy (like leastsignificant bit-planes).

The referenced shortcomings are not intended to be exhaustive, butrather are among many that tend to impair the effectiveness ofpreviously known techniques for generating an image processing; however,those mentioned here are sufficient to demonstrate that themethodologies appearing in the art have not been altogether satisfactoryand that a significant need exists for the techniques described andclaimed in this disclosure.

SUMMARY OF THE INVENTION

The present disclosure provides a novel steganographic algorithm fordifferent image models, and in particular, embedding secure data in alayer of a host image. In one respect, the algorithm may embed securedata within a best embeddable region of a layer (e.g., a selected colorlayer, a significant bit layer, etc.).

In one respect, the algorithm may provide an option of selecting thebest color models for embedding purposes. This achieves introducinghigher embedding capacity with less distortion as compared toconventional techniques. In addition to or alternatively, the algorithmmay provide the use of a complexity measure for cover images in order toselect the best of the candidate layers for the insertion of thesteganographic information.

In one embodiment, a cover or host image may be decomposed into severalcolor layers. The image may be converted into several color layersusing, for example, the following color models: RGB (red, green, blue),YCbCr (luminance, chrominance blue, chrominance red), YIQ (luminance,intensity, chrominance) HSV (hue, saturation, value), CMY (cyan,magenta, yellow), CMYK (cyan, magenta, yellow, black), and/or otherdocumented color models. In one respect, the image may be decomposedusing a best color model for a given image and secure data.

Once the image is decomposed, the embeddable regions may be selected. Inone respect, a layer or multiple layers are chosen from the selectedmodel. The layer(s) identified by the measure as the key color withinthe image are selected for embedding. It is noted that the color layermay vary with different images and color models.

Next, the selected embeddable layer may be categorized intonon-overlapping embeddable or non-embeddable regions. The identifiedregion may further be categorized for the measured upper boundary of themaximum capacity in bits that can be altered without causing perceptualdifferences. In one embodiment, the embedding procedure takes place inthe selected regions which may include multiple layer bit planes.Subsequently, a color reduction development step may be performed.

In other respects, the algorithm may embed secure data in a leastsignificant bit layer of a host image (e.g., a digital carrier 8-bitgrayscale or 24-bit color imagery). Areas of high noise resemblance maybe detected using a measure gauge local variation. Digital message(e.g., secure data) may be hidden by modifying the least bit layers ofbest host pixels. Subsequently, computational processes may beincorporated to retain the statistical structure that is associated withthe host image. A first order stego capacity measurement may be taken,which enables an optimal selection of the parameters to be appliedduring the embedding process. This measurement may evaluate the approachtaken and gives a resultant value which may depict the affects of theembedding process from a root mean square error and first orderstatistical point of view.

In one embodiment, a host image may be processed and analyzed accordingto the variation measurement. A stego file may be acknowledged and anextraction tag is formed to enable an extraction and reconstruction ofthe received stego image. The stego image may be converted into a binarystring and a threshold selection process may be performed based on, forexample, the size of the stego file to be embedded and/or the valuesdictated by a first-order stego capacity measurement (FOSCM). Theselected threshold process may ensure an even distribution of stegoinformation upon the host image and may select. In addition to oralternatively, the threshold value may provide security from differenttypes of detection processes.

Once host pixels from the host image have been identified, the data fromthe first stage may be forwarded to a second stage which embeds thesecure data. In one respect, the embedding process may occursimultaneously with a histogram retention algorithm used for staticallyor dynamically updating record of occurring events in the embeddingprocess. This may reduce the first-order statistical modifications. Forexample, if the insertion of the secure data requires changing of thevalue of the original pixel in the host image, the occurrence is noted.Simultaneously, every time a change in an original pixel is required,the records are queried and the pixel is modified to a value that maycounteract any previous changes. This step is possible due to the factthat, when a pixel value must be changed, there may be a lower pixelvalue and a higher pixel value that will reflect the desired bits in theleast significant values. A tag may subsequently be inserted, resultingin a host image ready for transmission.

In other respects, the algorithm may decompose a digital media or signalinto its binary format representation. Next, a pseudo noise sequence maybe generated using for example, M-Sequence, gold codes, chaotic, and thelike. The decomposed digital media or signal may be encrypted using, forexample, the pseudo noise (Pn) sequence. A Pn-sequence matrix may begenerated by segmentation of the pseudo noise sequence.

In addition or alternatively, a simultaneous compression and encryptionprocess whereby a digital media may be compressed is presented. Thetechnique may reduce transmission time and storage space of the signals.The encryption method/system includes the steps of converting an imageinto a binary bit stream; transforming the binary bit stream into binaryvector; and segmenting binary vector into various segments. Thecompression method/system includes the steps or generating a randomsequence (for example M-sequence, gold codes, etc.); developing a codematrix based on the random sequence; determining the distance measurebetween code matrix and each segmented binary vector, generating a keybased on the distance measure. Combine all the keys generated and appendthe keys used to segmenting and random sequence generating to obtain acompressed and encrypted image. Any encryption technique made on theimage decreases the redundancy, making the method/system suitable foruse in compressing encrypted images. In addition, this method/system maybe used to compress noise like signals, represent any multi-media data(e.g., images, audio, video, and other media sources). Thismethod/system may be applied for any application that requiresauthentication, data integrity, data confidentiality, and data security.

In embodiments where the digital media or signal includes highredundancy, the digital media or signal may be reconfigured with respectto pseudo noise sequence generated. Subsequently, the digital media orsignal may be correlated and applied to a logical operation between twoselected columns of the Pn-sequence matrix to generate a sequence closeto or equivalent to the segmented media.

The steps of segmentation and configuration may be repeated with a newsequence shift code and a selected column of Pn-sequence matrix until amaximum estimate of the original digital media or signal is completed.The algorithm may subsequently be recombined by reassembling the binaryformats of the shifts codes to generate a new compressed image.

To decode the compressed image, the compressed image may be decomposedinto its binary stream presentation. A Pn-sequence may be generatedusing information encrypted into the compressed image. A Pn-sequencematrix may be generated by segmentation of the Pn-sequence. Using twocolumns of the Pn-sequence matrix, a correlation of logical operationsmay be used to retrieve sequence closer to the digital media or signal.The original digital media may be retrieved by randomizing the digitalmedia or signal with respect to pseudo noise sequence generated if itwas reconfigured initially.

In one embodiment, the algorithm may be implemented on a processor,where the processor may be any computer-readable media known in the art.For example, it may be embodied internally or externally on a harddrive, ASIC, CD drive, DVD drive, tape drive, floppy drive, networkdrive, flash drive, USB drive, or the like. The processor is meant toindicate any computing device capable of executing instructions forreceiving the data from amongst other functions. In one embodiment, theprocessor is a personal computer (e.g., a typical desktop or laptopcomputer operated by a user). In another embodiment, the processor maybe a personal digital assistant (PDA) or other handheld computingdevice.

In some embodiments, the processor may be a networked device and mayconstitute a terminal device running software from a remote server,wired or wirelessly. Input from a user, detector, or other systemcomponents, may be gathered through one or more known techniques such asa keyboard and/or mouse. Output, if necessary, may be achieved throughone or more known techniques such as an output file, printer, facsimile,e-mail, web-posting, or the like. Storage may be achieved internallyand/or externally and may include, for example, a hard drive, CD drive,DVD drive, tape drive, floppy drive, network drive, USB drive, flashdrive, or the like. The processor may use any type of monitor or screenknown in the art, for displaying information. For example, a cathode raytube (CRT) or liquid crystal display (LCD) can be used. One or moredisplay panels may also constitute a display. In other embodiments, atraditional display may not be required, and processor may operatethrough appropriate voice and/or key commands.

In some respects, a method for producing a compressed and encryptedimage is provided. An image may be converted into a binary bit stream,and the bit stream may be decomposed into a plurality of segments (of afixed length and/or various lengths). A binary sequence based on a firstkey may be generated and may be used to generate a code matrix. Adistance between the code matrix and the distance may be determined foreach of the plurality of segments. Using a combined first and secondkey, a compressed and encrypted image may be obtained.

In other respects, a compressed image of a cover image may be provided.The cover image may be converting the cover image into a binary bitsteam. The bit streams may be decomposed into a plurality of segments(e.g., the plurality of segments having various lengths). Next, each ofthe plurality of segments may be classified using a redundancy of bitsbased on a first key to generate a plurality of redundant andnon-redundant segments. The redundant segments and non-redundantsegments may be compressed. In one embodiment, the non-redundantsegments may be compressed with low redundancy based on a second key.The combination of the compressed redundant and non-redundant segmentsmay form the compressed image of the cover image.

The terms “a” and “an” are defined as one or more unless this disclosureexplicitly requires otherwise.

The term “substantially,” “about,” and its variations are defined asbeing largely but not necessarily wholly what is specified as understoodby one of ordinary skill in the art, and in one-non-limiting embodiment,substantially and its variations refer to ranges within 10%, preferablywithin 5%, more preferably within 1%, and most preferably within 0.5% ofwhat is specified.

The term “coupled” is defined as connected, although not necessarilydirectly, and not necessarily mechanically.

The terms “comprise” (and any form of comprise, such as “comprises” and“comprising”), “have” (and any form of have, such as “has” and“having”), “include” (and any form of include, such as “includes” and“including”) and “contain” (and any form of contain, such as “contains”and “containing”) are open-ended linking verbs. As a result, a method ordevice that “comprises,” “has,” “includes” or “contains” one or moresteps or elements possesses those one or more steps or elements, but isnot limited to possessing only those one or more elements. Likewise, astep of a method or an element of a device that “comprises,” “has,”“includes” or “contains” one or more features possesses those one ormore features, but is not limited to possessing only those one or morefeatures. Furthermore, a device or structure that is configured in acertain way is configured in at least that way, but may also beconfigured in ways that are not listed.

Other features and associated advantages will become apparent withreference to the following detailed description of specific embodimentsin connection with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the office upon request and paymentof the necessary fee.

The following drawings form part of the present specification and areincluded to further demonstrate certain aspects of the presentinvention. The invention may be better understood by reference to one ormore of these drawings in combination with the detailed description ofspecific embodiments presented herein.

FIG. 1 show various bit plane decomposition and a Least Significant Bit(LSB) plane, in accordance with embodiments of the disclosure.

FIG. 2 shows a flow chart of a time domain data hiding technique, inaccordance with embodiments of the disclosure.

FIGS. 3A through 3C show images used for embedding secure data, inaccordance with embodiments of the disclosure.

FIG. 4 shows a general overview of Pseudo Noise Sequences, in accordancewith embodiments of the disclosure.

FIG. 5 shows a flow chart for encoding cover signal or data using PseudoNoise Sequence into a compressed signal or data (‘Simple Pn-sequenceencoder’), in accordance with embodiments of the disclosure.

FIG. 6 shows a flow chart for decoding compressed data using PseudoNoise Sequence to reconstruct the cover signal or data (‘SimplePn-sequence decoder’), in accordance with embodiments of the disclosure.

FIGS. 7A through 7C are various images used in an analysis, inaccordance with embodiments of the disclosure.

FIG. 8 shows a bit plane decomposition of the red layer of FIG. 7C, inaccordance with embodiments of the disclosure.

FIG. 9 shows a bit plane decomposition of the red layer of FIG. 7Crepresented using a M-sequence, in accordance with embodiments of thedisclosure.

FIG. 10 shows an embedding method, in accordance with embodiments of thedisclosure.

FIG. 11 shows selecting number of bits to embed based on level ofvariation, in accordance with embodiments of the disclosure.

FIGS. 12A and 12B show corruption of first-order statistics, inaccordance with embodiments of the disclosure.

FIGS. 13A and 13B show results of parameter selection, in accordancewith embodiments of the disclosure.

FIG. 14 shows relationship of parameter selection and number ofavailable pixels, in accordance with embodiments of the disclosure.

FIGS. 15A through 15J show test image, in accordance with embodiments ofthe disclosure.

FIGS. 16A and 16B show graph results of First-Order Stego CapacityMeasure, in accordance with embodiments of the disclosure.

FIG. 17 shows an embedded method, in accordance with embodiments of thedisclosure.

FIGS. 18A and 18B show noise detection, in accordance with embodimentsof the disclosure.

FIGS. 19A and 19B show threshold level settings, in accordance withembodiments of the disclosure.

FIGS. 20A and 20B show exponential relation between threshold andavailable pixels, in accordance with embodiments of the disclosure.

FIGS. 21A and 21B show test image, in accordance with embodiments of thedisclosure.

FIG. 22 shows a plot of histogram difference using adaptive embeddingmethods, in accordance with embodiments of the disclosure.

FIGS. 23A through 23D show histogram difference of Stego images with 10%information, in accordance with embodiments of the disclosure.

FIGS. 24A through 24D show histogram difference of Stego images with 80%information, in accordance with embodiments of the disclosure.

FIGS. 25A and 25B show histograms, in accordance with embodiments of thedisclosure.

FIGS. 26A through 26C show comparison histograms after embedding of 80%,in accordance with embodiments of the disclosure.

FIG. 27 shows a table calculation of FOSCM measure to find a bestcoverage image, in accordance with embodiments of the disclosure.

FIG. 28 show embeddable areas within an image and a detailed view ofallowable altered bits (highlighted) within a block. The left thumbnailshows the natural block and the right shows the highlighted areas, inaccordance with embodiments of the disclosure.

FIG. 29 shows a structure of palette based images, in accordance withembodiments of the disclosure.

FIG. 30 includes the root mean square error (RMS) values obtained bycomparing different clean images with their corresponding stego imageswhile embedding 6 k of data.

FIG. 31 contains the root mean square error (RMS) values obtained bycomparing different clean images with their corresponding stego imageswhile embedding 6 k of data.

DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

The invention and the various features and advantageous details areexplained more fully with reference to the nonlimiting embodiments thatare illustrated in the accompanying drawings and detailed in thefollowing description. Descriptions of well known starting materials,processing techniques, components, and equipment are omitted so as notto unnecessarily obscure the invention in detail. It should beunderstood, however, that the detailed description and the specificexamples, while indicating embodiments of the invention, are given byway of illustration only and not by way of limitation. Varioussubstitutions, modifications, additions, and/or rearrangements withinthe spirit and/or scope of the underlying inventive concept will becomeapparent to those skilled in the art from this disclosure.

Simultaneous Compression and Encryption of Noise Like Signals

In recent years, the applications of pseudo noise (Pn-) sequence arespreading through various fields and are increasing rapidly. Someparticular fields are communications (CDMA), cryptography, signaldetection, and identification in multiple-access communication systems.Pn-sequence is unique because of its spectral distribution and noiselike qualities of the waveform. A random stream shows similarstatistical behavior that of the same size Pn-sequences. The primeadvantages of a Pn-sequence are randomness and orthogonality of thesequence shifts which are employed in representation and compression ofthe random data/media.

Compression of random data is vital and difficult for data that has lowredundancy and whose power spectrum is close to noise. In most imagingoperations (such as medical, military, etc.) compression of all thebit-planes is vital. Since the least significant bit (LSB) planes datais random or has less redundancy, it is hard to compress using theconventional compression algorithms. Similarly, in steganography, thesecured information is embedded in the least significant bit planes ofthe cover image. Thus, the cover image with the secured data needs to bestored or transmitted over interne. But using conventional compressionalgorithms, the bit-plane information may be lost or compression of datais not effective.

In most data hiding cases, the stego/secured information needs totransformed or represented such that it is random before embedding intocover digital media. Thus, enhances the security level of the embeddedinformation.

In this disclosure, a novel combined compression and encryptiontechnique for noise like data using pseudo noise (Pn) sequence such asM-, Gold, and Kasami sequence is introduced. First, a set of randomsequences may be generated. These Pn-sequences may be broken intovarious segments of same length as that of input. If the data isredundant, then the data is reconfigured using a Pn-sequence. Thisdecreases the redundancy of input data and thus, makes data moresuitable for the compression. These operations may result in acompression and encryption. Further, the proposed algorithm may beapplied to various random data used in other applications.

Two Algorithms for Compressing Noise Like Signals

As noted above, compression is a technique that is used to encode dataso that the data needs less storage/memory space. Compression of randomdata is vital in case where data needed to be preserved has lowredundancy and whose power spectrum is close to noise. In case ofsteganographic images, the least significant is of high importance uponcompressing with lossy algorithms the LSB might get lost. Since the LSBplane data has low redundancy, lossless compression algorithms may beaffecting a good compression ratio. These problems motivated indeveloping a new class of compression algorithms. In this disclosure,compression technique are provided for compressing random data likenoise with reference to known pseudo noise sequence generated using akey. Furthermore, the proposed algorithms can be extended to all kindsof random data used in various applications.

Run length, Huffman code, Arithmetic coding, and the like are effectivein compressing data that has redundancy. But if the data to becompressed is not redundant or random in nature, then the existingcompression algorithms may not have a high compression ratio. Forexample, consider various bit-planes of an image as shown in the FIG. 1.In most medical imaging operations compression of all the bit-planes isvital. Since the LSB planes data is random or has less redundancy hencecompression ratio using the normal compression algorithms is minimal.

Steganography is a process of secretly embedding secured information ina harmless digital media without changing its perceptual or audiblequality. Digital steganography and watermarking are currently activeresearch areas in signal processing that deals with encompassing methodsof copyright protection, image authentication, and securedcommunications. Several algorithms for data hiding in digital media havebeen proposed in both the time and frequency domain. The presentdisclosure provides techniques in the time domain where the secured datais embedded in the least significant bit planes (LSB planes) of thecover image. A simple block diagram scheme for embedding secured datainto the cover image is as shown in FIG. 2.

For example, consider a LSB plane of a cover image, the secured data tobe embedded and the cover image's LSB with the embedded secured data asshown in FIGS. 3A, 3B, and 3C. FIG. 3A shows a least significant bitplane of a cover image. FIG. 3B shows a road map image to be imbedded.FIG. 3C show a LSB after embedding the image shown in FIG. 3B to FIG.3A. The information in the cover image with stego LSB plane may berandom and may have minimal redundancy, therefore, normal compressionalgorithms are not effective. The cover image with the secured data mayneed to be stored or transmitted over Internet.

In some embodiments, some particular fields like communications (CDMA),cryptography, and signal detection and identification in multiple-accesscommunication systems need additional or optional image processing. Thepresent disclosure use M-sequence because of the autocorrelation,spectral distribution, and noise like qualities of the waveform forthese and other application. A random sequence generated using a pseudonoise generator may be employed in coding the secured data and then isadded to LSB plane of the data message. The prime advantages of anM-sequence with a high period are an excellent effect of randomness andassociated autocorrelation properties. The PN sequences and M-sequenceis described in more details below.

Pseudo Noise (Pn) Sequences

In this disclosure, a compression technique that compresses the randomdata like noise with reference to binary pseudo noise sequence generatedis provided. A first algorithm “Simple Pn-sequence based compression”may utilize a Pn-sequence for representing the random digital media withrespect to keys. The logical operations may be considered for decreasingthe errors arise during the decoding processing. The total randomdigital media length may be considered for compressing hence it providesa high compression ratio.

In other respects, a second algorithm, “Multi Pn-sequence basedcompression” proposes the whole random data may be broken into varioussegments, allowing for the encoding of any kind of randomness in thedigital media. This technique may use a combination of randomizing andcompression of the digital media, reducing errors in decoding and cansuccessfully compress any kind of digital media.

In this section, a general definition and overview of the Pseudo Noise(PN) Sequences of the present disclosure is provided. A pseudo noisesequence, as used and defined herein, is a periodic binary sequence thatis normally generated using linear feedback shift registers and welldefined logic. These sequences may be semi-random, i.e. they are randomin a particular period, but after each period, the sequences may repeatitself. The waveform of PN sequences may be similar to noise-likewaveform. A Pseudo Noise sequence of sufficient length may have similarproperties of a random data stream. The various Pseudo noise sequencesthat are commonly used are M-sequence, Gold codes and etc., theM-sequence is simplest in form of the pseudo noise sequences. FIG. 4shows in general, an over view of various pseudo noise sequences.

M-sequence is a pseudo random binary sequence that may be generated by alinear feedback shift register with a generating polynomial (primitive)in which coefficients arise from Galois field. If the period of thepseudo random binary sequence is equal to about 2^(n)−1 (n is the degreeof the polynomial), it is called a maximum-length sequence (orM-sequence). An M-sequence may also be defined as a maximum-length shiftregister sequence. M-sequence is one of the simplest pseudo noisesequences. M-sequence may include various properties including:

-   -   1) Runs of M-sequence:—The numbers of runs of one's is equal to        runs of zero's. In addition, the number of the total runs of an        M-sequence of order “N” is given by        The total number of runs=2^(N-)1  Eq. 1        -   Note: −½ the runs will be one bit long;            -   ¼ the runs will be 2 bits long;            -   ⅛ the runs will be 3 bits long; up to a single run of                zeroes that is ‘N−1’ bits long and a single run of ones                that is ‘N’ bits long.            -   Example:—Consider an M-sequence of order “4” of Tap code                [3, 4] and then find the total number of runs.        -   Solution:—M-sequence of Tap code [3, 4] is [1 1 1 1 0 0 0 1            0 0 1 1 0 1 0]        -   a) The total runs are 2⁴⁻¹=8. From the above Sequence the            runs are shown as [4 3 1 2 2 1 1 1] i.e., 8.        -   b) The Number of runs for ones is 4 and the number of runs            for zeros is 4. Hence forth, the number of runs for one=the            number of runs for zero.

Runs Zero's One's 4 — 1 3 1 — 2 1 1 1 2 2

-   -   2) Balance Property:—The number of ones is one more than the        number of zeros in an M-sequence of order ‘N’.        -   Example:—Consider an M-sequence of order “4” of Tap code [1,            4] then find the total number of runs.        -   Solution:—M-sequence of Tap code [1, 4] is [1 1 1 1 0 1 0 1            1 0 0 1 0 0 0];        -   a) Number of ones in the above sequence=8;        -   b) Number of zeros in the above sequence=7;        -   Hence forth, number of ones=number of zeros+1.    -   3) Shift and Add property:—It is a well known property in which        the modulo-two addition of any two identical M-sequences with        different phases generates another identical M-sequence, but        with another phase.        -   Example:—Consider an M-sequence of order “4” of Tap code [1,            4] then find the total number of runs.        -   Solution:—M-sequence of Tap code [1, 4] is [1 1 1 1 0 1 0 1            1 0 0 1 0 0 0];        -   The shifted M-sequence after a shift two to right is [1 1 0            1 0 1 1 0 0 1 0 0 0 1 1];        -   Upon Adding both the M-sequence and the shifted M-sequence            is:        -   [0 0 1 0 0 0 1 1 1 1 0 1 0 1 1] i.e. a shifted M-sequence            with shift of ‘7’ to right.    -   4) Correlation:—M-sequence has good bi-level auto-correlation        characteristics with values of −1 and N, but the        cross-correlation property of the M-sequences is relatively        poor.        -   Algorithm

In this section, a new class of compression algorithms based on theM-sequence which compress random data (for example, bit-planes withsecured information or LSB planes of the cover image) is presented. Theblock diagram for ‘Simple Pn-sequence encoder’ is presented in FIG. 5.

The general encoding steps of compression algorithm include inputtingthe digital media or signal and decompose into its binary formatrepresentation. Next, a PN-sequence (M-sequence) may be generated from apseudo noise generator using a key and generate the Pn-sequence matrix.The generated sequence may be correlated and logical operations betweentwo selected columns of a Pn-sequence matrix may be applied to generatea sequence close to the cover signal. The correlation and logicaloperation step may be repeated with a new sequence code and a selectedcolumn of Pn-sequence matrix until fixed iterations are achieved. Thebinary formats of the new sequence code may be recombined to generate anew compressed image.

The generated compressed image may have no relationship with the coverimage. In one embodiment, to retrieve the information from compressedthe image may be done by processing the compressed using decodingalgorithm and the set of keys. The block diagram for ‘Simple Pn-sequencedecoder’ is presented in the FIG. 6.

The general decoding steps include inputting compressed keys anddecompose the keys into its binary format representation. A pseudo noisesequence (M-sequence) may be generated from a pseudo noise generatorusing a key and generate the Pn-sequence matrix. The generated sequencemay be correlated and logical operations between two selected columns ofa Pn-sequence matrix using the key to retrieve a sequence similar to acover signal. The correlation and logical operation step may be repeatedwith a new sequence code and a selected column of Pn-sequence matrixuntil fixed iterations are achieved. The binary format of final sequencecode from the previous step may be recombined to retrieve the coverdigital media or signal.

The ‘Simple Pn-sequence based compression algorithm’ may compress randomdata successfully, however, the decoding algorithm may introduce errorson reconstruction of the original data, and also when the data has highredundancy. The algorithm may not be effective and may introduce ahigher amount of errors on reconstruction of data. This motivated indeveloping ‘Multi Pn-sequence based compression algorithm’ which hassimilar frame work as ‘Simple Pn-sequence based compression’ algorithmbut includes segmentation block and representation blocks in order toover come the above problems discussed above.

Segmentation Block

The segmentation block takes the input M-sequence which may be used as areference for compressing the data. The segmentation block may be brokeninto various segments having same length as that of input. In onerespect, the number of reference sequences generating multiplePn-sequences that are defined by a key may be increased. In addition oralternatively, the compression ratio of the data may be increased andthe errors arise during the reconstruction of the original data may bedecreased. T The segmentation block's output sequences may also definedby a key.

Data Configuration Block

The data configuration block may take the data and Pn-sequence as inputsand reconfigures the data thus decreasing the redundancy of input dataand making data more suited for the compression. The ‘Multi Pn-sequencebased compression algorithm’ increases the compression ratio and alsominimizes the loss of data during the decoding of the compressed data.The general encoding steps of compression algorithm includes inputtingdigital media or signal and decompose the data into its binary formatrepresentation. A pseudo noise sequence (M-sequence) from a pseudo noisegenerator using a key may be generated and a Pn-sequence matrix bysegmentation of sequence may be constructed. The digital media may bereconfigured by randomizing the media with respect to pseudo noisesequence generated. The reconfigured data may be correlated and logicaloperations may be between two selected columns of Pn-sequence matrix togenerate a sequence close to the segmented signals. The correlation andlogical operation step may be repeated with a new sequence code and aselected column of Pn-sequence matrix until fixed iterations areachieved. The binary formats of the keys may be recombined to generate anew compressed image.

The general decoding steps of compression algorithm include inputtingthe compressed keys and decompose the keys into its binary formatrepresentation. Next, a pseudo noise sequence (M-sequence) from a pseudonoise generator using a key may be generated and a Pn-sequence matrixmay be constructed by segmentation of sequence. For two columns ofPn-sequence matrix a correlation step and a logical operation step maybe applied using the key to retrieve a sequence close to the coversignal. The correlation and logical operation step may be repeated witha new sequence code and a selected column of Pn-sequence matrix untilfixed iterations of the key are achieved. The original digital media maybe retrieved by randomizing the media with respect to pseudo noisesequence generated. Binary formats segments may be recombine to retrievethe reconstructed digital media or signal.

Computer Simulation

For computer simulation, the method of the present disclosure was testedover 50 images of various sizes from NSP lab's image database andvarious random signals/data. The experimental results are presented forsome of the images of various sizes and the random data for varyinglength. The various images utilized for simulation are shown in FIGS.7A, 7B, and 7C (FIG. 7A is a road map image, FIG. 7B is a satellitepicture of the river, and FIG. 7C is a picture of a little boy). Inaddition, random digital data generated for various lengths (16 to 512)were tested.

Table 1 below presents the simulation results using ‘Simple Pn-sequencebased compression’ algorithms for compressing digital image LSBbit-planes and various lengths of data generated for 3 trials. Thevarious images with varying sizes are utilized and the last threebit-planes are used in simulation testing. ‘Bit-plane 1’ indicates theleast significant bit planes and follows. In addition, the simulationresults for 3 trials are presented in case of random data since at eachtrial the randomness of data obtained is different.

TABLE 1 Simulation results using the proposed (‘Simple Pn-sequence basedcompression’) algorithm Random Image Bit- Bit- data (size) plane1 plane2Bit_plane3 length Trial 1 Trial 2 Trial 3 Boy 0.869 0.922 0.942 31 0.903(28) 0.903 (28) 0.742 (23) image (512 × 512) River 0.873 0.915 0.949 630.667 (42) 0.857 (54) 0.810 (52) (256 × 256) Road map 0.891 0.923 0.959255  0.824 (210)  0.769 (196)  0.714 (182) (128 × 128)

From Table 1, the least significant bit planes are effectivelycompressed. In addition, for high significant bit-planes, the redundancyin the bit plane may increase, thus decreasing the compression ratio.Hence we developed ‘Multi Pn-sequence based’ compression algorithm whichrandomizes and segments the image information thus decreasing theredundancy. The segmentation block and the data configuration blocks arevital.

For example, a comparison between the bit-planes for FIG. 7C of size 512by 512 was performed. FIG. 8 shows the original bit-plane decompositionfor the red layer of FIG. 7C. FIG. 9 shows the same bit planes of FIG.7C's red layer after the data is reorganized using the datareconfiguration block. The redundancy in case of high significantbit-planes may be reduced and also the new classes of bit-planes mayhave similar similarities thus increasing the compression ratio of thewhole image.

Table 2 below presents the simulation results using ‘Multi Pn-sequencebased compression’ algorithms for compressing digital image LSBbit-planes and various lengths of data generated for 3 trials, inaccordance with embodiments of the present disclosure. The variousimages with varying sizes are utilized and the last three bit-planes areused in simulation testing. ‘Bit-plane 1’ indicates the leastsignificant bit planes and follows. In addition, the simulation resultsfor 3 trials are presented in case of random data since at each trialthe randomness of data obtained is different.

TABLE 2 Simulation results using the proposed (‘Multi Pn-sequence basedcompression’) algorithm Random Image Bit- Bit- data (size) plane1 plane2Bit_plane3 length Trial 1 Trial 2 Trial 3 Boy 0.924 0.843 0.878 310.87096 (27)  0.742 (23) 0.742 (23) Image (512 × 512) River 0.859 0.8910.913 63 0.714 (45) 0.778 (49) 0.778 (49) (256 × 256) Road map 0.8850.895 0.899 255  0.745 (190)  0.729 (186)  0.761 (194) (128 × 128)

TABLE 3 Simulation results comparison between the proposed algorithm 1and algorithm 2 and the Run length coding Digital Proposed Proposed Runmedia algorithm1 algorithm2 length Boy Bit- 0.869 0.924 1.481 Imageplane1 (512 × 512) Bit- 0.922 0.843 1.430 plane2 Bit- 0.942 0.878 1.731palne3 River Bit- 0.873 0.859 1.483 (256 × 256) plane1 Bit- 0.915 0.8911.488 plane2 Bit- 0.949 0.913 1.477 palne3 Road map Bit- 0.891 0.8851.414 (128 × 128) plane1 Bit- 0.923 0.895 1.383 plane2 Bit- 0.959 0.8991.367 palne3

Table 3 shows the comparison between the proposed algorithms to theconvention run length function. The comparison results prove that therun length algorithm actually needs more memory for storing the datathan the actual image. For example, for storing the boy's imagebit-plane 1′ of size 512 by 512, run length needs a matrix of size 757by 757 (approximately) where as the proposed algorithm 2 (MultiPn-sequence based) needs a matrix of size 473 by 473 (approximately),thus showing that the proposed algorithms provide a better compressionratio for random data.

In conclusion, the compression algorithms of the present disclosure,‘Simple Pn-sequence based’ and ‘Multi Pn-sequence based’ have beenpresented for compressing random data. Steganographic images leastsignificant is of high importance but compressing using lossy algorithmsthe LSB plane might get lost. The algorithms are effective incompressing the LSB plane data. In addition, the bit-planes arerepresented with reference to the Pn-sequence thus making the data morerandom. This is better suited for compression using the proposedcompression algorithms. The amount of memory size required for storingthe ‘Boy image bit-plane 1’ with the ‘Simple Pn-sequence based’algorithm has a size of about 512 by 512, whereas the run lengthtechniques needs a matrix of size about 757 by 757. The ‘MultiPn-sequence based’ algorithm needs a matrix of size 473 by 473(approximately). In addition, the compression ratio of the algorithms ofthe present disclosure may be improved by using conventional compressionalgorithms for bit-planes with high redundancy of the data (such as mostsignificant bit-planes).

T-order Statistics and Secure Adaptive Steganography

Adaptive steganography is a statistical approach for hiding the digitalinformation into another form of digital media. The goal is to ensurethe changes introduced into the cover image remain consistent with thenatural noise model associated with digital images. There are generallytwo classes of steganography—global and local. The global classencompasses all non-adaptive techniques and is the simplest to apply andeasiest to detect. The second classification is the local class, whichdefines most of the present adaptive techniques.

Image steganography is the art of concealing any binary representabledigital media into a digital cover image. The information intended to gounseen by intermediate parties is termed the steganographic content.Hidden data formats are to include: audio signals, video files, textdocuments, as well as other digital images. Currently, steganographytechniques can be divided into two distinct classes such as non-adaptiveand adaptive methods. Non-adaptive steganography methods arepredominantly simple embedding algorithms that at most incorporate apseudo-random pattern in the embedding of secret information within animage. Such methods belonging to this category are readily available onthe internet. WbStego, SecureEngine, and Stool are three of the mostcommonly referenced and commercially available methods. Embeddingapproaches such as these make little, if any, acknowledgement to thestatistics and visual features associated with the cover image. As aresult, the resultant stego image is easily suspected of holdingembedded information and very often is susceptible to localizationtechniques.

The other class of embedding approaches, adaptive steganography, is anever evolving topic within the scientific field of steganography. Therehave been numerous adaptive methods proposed over the last few years.Current techniques apply a scanning window measuring local standarddeviation. A threshold is set and is the decisive factor in selectingwhich cover image pixels to use for hiding stego information. A notablelimit to the amount of information that may be securely embedded usingthe given approach is also used.

An alternate image scanning structure using regional segmentation and acomplexity measure to adaptively select embedding locations is alsocurrently being used. Additionally, a more intensive approach whereconsiderations are made to retain first and second order statistics isbeing applied. Histogram frequencies are maintained by modeling theembedding process as a Markov source and modifying the information to beembedded so that it resembles the ideal distribution pertaining to thecover image. There are two limitations generally associated withadaptive embedding techniques: 1) a limited allowable embeddingcapacity, 2) methods thwarting one type of stego detection tool arevulnerable to another.

The present disclosure provides an adaptive technique that is able toovercome embedding capacity limitations and reduce the revealingartifacts that are customarily introduced when applying other embeddingmethods. In one respect, a third faction which is the pixel focusedclass of steganography is used. Applying a new adaptive T-orderstatistical local characterization, the algorithm may be able toadaptively select the number of bits to embed per pixel. Additionally, ahistogram retention process, an evaluation measure based on the coverimage and statistical analysis may allow for the embedding ofinformation in a manner which ensures soundness from multiplestatistical aspects. Based on the results of simulated experiments, thepresent disclosure shows how to securely allow an increased amount ofembedding capacity, simultaneously avoiding detection by varyingsteganalysis techniques.

In one respect, a new adaptive embedding technique that has been shownto reduce the probability of detection of steganographic content withindigital images. Through directional, local T-order statisticalcharacterization and first order statistical retention, the algorithmmay be able to foil steganalysis attempts of different natures andsimultaneously increase the allowable embedding capacity. Analysis andguidance are offered by a first-order stego capacity measure (FOSCM)that assists in, for example, the selection of which pixels to use forthe embedding of information, the number of pixels to embed per pixel,the selection of which bit layers to hide information, the manner inwhich to embed information, and/or the selection of a cover image from adatabase of candidate cover images varying in size, format, and imagefeatures.

In other respects, all data acquisition devices incur some level ofnoise, such as the quantization error introduced by digital cameras.Additionally, the changing frequencies of the color spectrum throughoutan image also contribute to the presence of a natural noise level withina digital image. The algorithm may be designed to find locations withinan image where these concepts are most prominent. Pixels lying withinregions characterized by chaotic variation, not structured variationfound in edge pixels, may be identified, and may be further investigatedto decide how many bits to embed and which bit layers to use in theembedding process. The algorithm may apply an equivalent amount ofanalysis in order to enact efforts to retain the first-order statisticalstructure of the cover image. The fundamental structure of the algorithmcan be represented by the block diagram in FIG. 10.

Referring to FIG. 10, the algorithm includes several stages producing anelaborate embedding process. In Stage I, development of T-orderstatistical directional variation and parameter selection process may bepreformed. Steps including, but is not limited to, selection ofembedding pixels, selection of number of bits per pixel to embed, andselection of which bit layers to embed in may be performed.

In Stage II, a first-order statistical retention may be performed. Stepsinclude, but are not limited to, first-order stego capacity measurecomputer simulations, analysis, and conclusions.

Stage I—T-Order Statistical Directional Variation Measure and ParameterSelection Process

The advantage associated with the new measure is the ability to amplifythe level of local variation that is associated with the vicinity of agiven pixel. One of the foremost complications involved in theformulation of an adaptive steganographic algorithm is integrating ameans for allowing the embedded information to be extracted. Specificpixels may be selected to carry stego information. One commonly appliedsolution is to remove the influence of any pixels that are to beanalyzed from the selection process. If bits are only to be changed inthe least significant bit layer, than all bits in that layer may bezeroed out or all set to 1. In this manner, local variation measures maybe irrelevant to the least significant bits' contribution to the pixelvalue. When investigating this process closer and when considering theinsertion of information into multiple bit layers, there may be aresultant color reduction. Zeroing out the entire least significant bitlayer converts all odd valued pixels into even ones. The result may beamplified when multiple bit layers are cleared. The benefit ofincorporating a T-order statistical analysis may be an amplification ofthe local noise measurements. T-order statistics add sensitivity to thelocal variation measures. Removing the affected bit layers removesvariation from the original cover image. The purpose of t-orderstatistical analysis is to offset this fault to reinstate a moreaccurate calculation of local variation.

In one respect, an image may be scanned by a window of size 3×3,although other dimensions may be used. A small window may ensure thatvariation calculation is as local as possible giving an accuratecharacterization of the information within the vicinity of a pixel. Amodified variance may be used as a characterization of the variationwithin the vicinity of a pixel. Instead of using the mean of theacknowledge intensities, a T-order value may be used as a substitute.The associated equation is given below.

$\begin{matrix}{{{\eta\left( {i,j} \right)} = {\frac{1}{N}{\sum\limits_{n = 1}^{N}\left( {x_{n} - {{Torder}\left\{ x \right\}}} \right)^{2}}}},} & {{Eq}.\mspace{14mu} 2}\end{matrix}$where η is the variable assigned to the calculated local median-basedvariance. The location of the pixel of interest within the image isdesignated by i and j. The set of pixels within the window is x and N isthe total number of pixels within the window. The window size used is a3×3, where the center value is the pixel for which the variation measureis calculated. A small window may be preferred in order to maintain theabsolute locality of the variation measure. It may not be rational toconsider a pixel as being within a high variation region when thelocation of such may be actually three pixels away from an area trulycontaining a sufficient level of noise. Antonymous may be the minimumrestriction of the window size. In order to account for variations inall directions, the specified window size may be used. A 2×2 windowwould only consider four directions compared to the eight directionsincluded in a window of size 3×3. Additionally, the variation measure ofa 2×2 sample space would pertain to a pixel whose location was displacedby half a pixel both horizontally and vertically.

The pixel used for the embedding of stego bits may be defined as thebest. If the calculated variation for a particular pixel exceeds thethreshold by a determined magnitude, the pixel may be used for theinsertion of multiple bits. This principle may be the key factor in theincrease in the embedding capacity. In addition, to the increase inembedding capacity, the insertion of multiple bits may also assist inthe retention of first order statistics. Embedding multiple bits withina cover image pixel essentially results in a reduced number of pixelvalues that are altered. Thus, there are fewer modifications to thefrequencies in the histograms. FIG. 11 demonstrates the general conceptbehind the adaptive embedding process.

Furthermore, when multiple bits are inserted into a pixel, thereassigned value may be a non-adjacent value, diffusing necessaryadjustments to a further distance. Thus, the random spikes introduced byother methods are beneficially avoided. A figure displaying the randomspikes that occur within the histogram after embedding stego informationis given below in FIGS. 12A (a histogram of stego image) and 12B (ahistrogram of a clear copy image).

The importance of the parameter selection process is to ensure an evendistribution of stego information throughout the image, dependant on thesize of the stego file that is to be embedded. Neglect of this factor inthe embedding process may lead to regions in the image carrying a highdensity of stego information. Concentrated regions of altered pixelsmake the stego image susceptible to detection and also simplify anylocalization attempts. To facilitate extraction, information may beembedded from left to right and from top to bottom. Failure to selectthe proper parameters is demonstrated below in FIGS. 13A (excessivelylow parameters) and 13B (adaptive parameter selection).

Selection of the parameters may be dependent on two dictating factors.First, the size of the stego file that is to be embedded in number ofbits. The second is the degree of magnitude that separates the singlebit embedding threshold from the multi-bit embedding threshold. Therelationship between the two thresholds and the number of availablepixels may be represented by the equation given below.Π=Δe ^((-ατχ))+β  Eq. 3.

The number of available pixels Π may be found using the exponentialfunction displayed above. Δ is the total number of pixels in the coverimage, α is an image dependent variation factor, β is the ratio of themulti-bit threshold to the single bit threshold, χ is the single bitthreshold, and β is the number of pixels with no local variation. Thisrelationship is represented by the 3-dimensional graph below shown inFIG. 14.

There are several factors to consider when determining the values of theparameters. From the perspective of τ which is the ratio of themulti-bit threshold to the single bit threshold, there are benefits anddisadvantages to having both a low ratio and a high ratio. Associatedwith a low value of τ include more multi-bit carrying pixels, a higherimage capacity, a higher root mean square error, and/or a reduced numberof changes in first-order statistics. Associated with a high value of τinclude a reduced number of multi-bit carrying pixels, a lower imagecapacity, a lower root-mean square error, and/or an increase in numberof change in first-order statistics

The root-mean square error is a measure of how much change wasintroduced into the image and is one manner of rating the probability ofdetection. The structure of first-order statistics may also be animportant aspect to consider when aiming increasing resistance todetection attacks. A means of selecting the best balance between all thefactors to be considered is presented in a following section. Histogramretention is another factor that warrants thorough consideration.

A final procedure to consider is the manner in which to embed theinformation with focus on a pixel and its associated bit layers. Bitsmay be inserted as far up as into the third least significant bit layer.Given the proper level of measured variation, the insertion of multiplebits and evidently a greater change in the value of a pixel will not bea cause for risen suspicion.

Stage II—First-Order Statistical Retention

Selecting the proper thresholds may predetermine the locations of everypixel that may be used for embedding stego information and once this hasbeen determined, the image may then be prepared for the second stage ofthe algorithm. One objective of the second stage is to minimize anychanges that must be unfortunately introduced into the image during theembedding process. While the insertion of stego information is takingplace, records may be created and updated to remember the actions thathave taken place. The focus of these efforts may be to retain thestructure of the first-order statistics associated with a cover image.Analysis of first-order statistics is a common approach taken bydetection methods and making efforts to maintain these characteristicswill assist in evading detection. From FIGS. 12A and 12B, it was shownthat embedding information without taking precautions adds random spikesto the frequencies of the histogram. In Stage II, a vector may becreated monitoring all pixel value changes. When a pixel is to bechanged, the vector may be checked and the possible new values whichreflect the required bit/bits are queried. The pixel with the lowerassociated number in the vector may become the new pixel value. Thesteps defining this procedure are given below.

In one respect, the technique includes identifying associated bit\bitswith current qualified pixel. If the result is identical, there isnothing else to do. If not, find the two nearest pixel values with therequired bit\bits. Next, a query data vector for recorded values of twonearest pixels may be performed. A conversion from a pixel to pixelvalue with the lower associated number may be performed. The vector maybe updated by incrementing number for the selected pixel value. Finally,the number associated with original value of the converted pixel isdecremented and stego bit with appropriate records update may beoutputted.

First-Order Stego Capacity Measure (FOSCM)

The First-Order Stego Capacity Measure may enable an optimal decision inthe balance between RMSE and first-order statistics. With this measure,the best cover image for a specific stego file among a database ofimages varying in size, format, and features. Simultaneously, differentexisting embedding algorithms to determine the optimal performer may betested. In order to calculate the FOSCM, a histogram may first becreated for the stego image and the original cover image. The followingequation is then applied.

$\begin{matrix}{{H = {\sum\limits_{k = 1}^{K}\;{{{x(k)} - {y(k)}}}}},} & {{Eq}.\mspace{14mu} 4}\end{matrix}$where x and y represent the histograms of the stego and original coverimage. K is the total number of possible intensities which for an 8-bitimage or color layer is 256. H is simply the sum of all differencesintroduced by the stego process between the two histograms. Uponderivation of the H value, the FOSCM may be calculated using

$\begin{matrix}{{\Omega = \frac{{RMSE}*H*\lambda}{\Lambda}},} & {{Eq}.\mspace{14mu} 5}\end{matrix}$where Ω represents the FOSCM. The variable λ is the number of bitsresulting from the conversion of the stego file into binary. A is thetotal number of pixels in the cover image. With the calculation of Ωcomes the ability to simultaneously minimize RMSE and altering offirst-order statistics.Computer Simulation and Analysis

Computer simulations can be simulated in MATLAB software package. Rawquick pairs (RQP) and RS-steganalysis detection algorithms may bereduplicated versions of the original algorithms. The various testimages used in this analysis are 512×512 color images shown in FIGS. 15A(squirrel), 15B (blue coast), 15C (bluebonnets), 15D (building), 15E(hen and rooster), 15F (Golden Gate Bridge), 15G (fish), 15H (rock), 15I(boy image), and 15J (trolley)

The simulation results show that the presented method is in generalimmune to LSB detection methods. The ‘Ω’ for cover image shown in FIG.15D of size 512×512 for various values of ‘T’ & various percentages ofembedding information are presented in the table 1. The best T-ordervalue for 10% of hidden information into cover of ‘Building’ is 1. Inaddition, the best T-order value for 33% of hidden information intocover of ‘Building’ is 2 and the best T-order value for 33% of hiddeninformation into cover of FIG. 15D is 9.

TABLE 4 ‘Ω’ value for percentages of hidden for cover ‘Building’ varyingT-order values Hidden T-order values data % 1 2 3 4 5 6 7 8 9 10% 157.83463.49 763.66 1139.6 1229.6 1195.6 798.69 450.22 164.63 33% 6210.25897.9 6086.4 6037.6 6012.3 6029.5 6227.6 6112.8 6448.3 50% 11367 1119411601 11381 11199 11521 11283 11086 11059

FIGS. 16 A and 16B show various ‘Ω’ value for varying percentages ofhidden for cover FIG. 15E with varying T-order values. FIGS. 16A and 16Billustrates the variations in the ‘Ω’ value for varying percentages (for10% and 30% of the stego information) is embedded into the cover image.The best T-order value for 10% of hidden information into cover of FIG.15E is 2. And in case for, the best T-order value for 33% of hiddeninformation into cover of FIG. 15E is 9.

TABLE 5 RQP Steganalysis Attack - Estimated probability of embeddedinformation in each layer Blue Coast Cover with 50% hidden informationT- Reliability Estimation order Ratio 0% 1% 5% 10% 20% 50% = 1 0.9983639.42% 9.53% 0.16% 0.27% 1.39% 49.22% = 2 0.99754 66.76% 15.83% 0.26%0.37% 1.15% 15.63% = 3 0.99707 74.96% 17.58% 0.28% 0.37% 0.88% 5.93% = 40.99788 57.07% 13.65% 0.22% 0.35% 1.31% 27.40% = 5 0.99809 49.43% 11.88%0.20% 0.32% 1.37% 36.80% = 6 0.9983 41.58% 10.05% 0.17% 0.28% 1.39%46.53% = 7 0.99766 63.79% 15.17% 0.25% 0.37% 1.21% 19.21% = 8 0.9978159.37% 14.17% 0.23% 0.35% 1.28% 24.59% = 9 0.99817 46.57% 11.21% 0.19%0.31% 1.38% 40.34%

To test the security associated with the new proposed algorithm,implementations of RS steganalysis and Raw Quick Pairs (RQP) may besimulated. Table 5 provides the detection results when applying RQPsteganalysis for a ‘blue coast’ cover with 50% hidden information forvarious t-order values. The results in Table 6 show that in comparisonto other embedding techniques, both random and adaptive, the method ofthe present disclosure is able to avoid detection in all simulationsexcept for one. The clean cover images were also tested to have somemanner of control group for comparison with the gathered results.

TABLE 6 RS Steganalysis Attack - Estimated Length of Detected MessageSecure Non- New Method Clean Engine Adaptive Adaptive II - No LSB ImagesFish 10% 159 1,782 5,008 238 291 Fish 33% 793 11,707 12,022 170 291 Fish50% 1824 17,105 17,596 1123 291 Gold 10% 4,419 7,125 5,129 160 198 Gold33% 16,905 7,125 12,876 181 198 Gold 50% 15,939 10,027 18,831 200 198Rock 10% 9,947 2,585 5,209 685 743 Rock 33% 15,810 12,800 12,675 689 743Rock 50% 11,782 18,472 17,935 635 743 Boy Image 1,655 808 3,346 −518−472 10% Boy Image 2,845 10,378 10,692 −456 −472 33% Boy Image 3,07215,248 16,724 −393 −472 50% Trolley 10% 1,631 1,676 4,641 −18 −46Trolley 33% 12,060 9,519 12,475 69 −46 Trolley 50% 16,821 13,949 17,394306 −46

The above provides an adaptive method of steganography that successfullyreduces the probability of detection while simultaneously allowing ahigher image capacity when compared to other adaptive embeddingtechniques such as the algorithms presented by Fridrich and Rui as wellas existing random embedding methods. This may be accounted to theincorporation of t-order statistics in order to improve upon thesensitivity of the local variation measure calculations to offset theresolution reduction caused by the removal of bit layer information.Using the adaptive multi-bit principle, more information is able to beinserted which has the additional benefit of reducing the necessarychanges of necessary first-order statistics as a result of utilizing areduced number of cover image pixels. Additionally, the adaptiveassignment of the threshold in order to allow for an ideal distributionof stego information throughout the image further assists in thedecreasing in the risk of detection. Finally, by incorporating a conceptpresented by Franz, we successfully resist the changing of first-orderstatistics thwarting any histogram based steganalysis attacks. Figuresshowing the histograms of outputted images demonstrate the reduction inidentifiable artifacts commonly introduced by the embedding process. Asan added feature, a First-Order Stego Capacity Measure (FOSCM) has beenprovided which optimizes the balance between RMSE and disturbances offirst-order statistics.

Adaptive Steganography with Increased Embedding Capacity for NewGeneration of Steganographic Systems

Adaptive steganographic techniques have become a standard directiontaken when striving to complicate the detection of secret communication.The consideration of cover image features when embedding information isan effort to insert digital media while keeping the visual and thestatistical properties of the cover image intact. There are several suchembedding methods in existence today, applicable for different formatsof images and with contrasting approaches. In this disclosure, a newadaptive embedding technique which alters the least significant bitlayers of an image. This technique is driven by three separatefunctions: 1) adaptive selection of locations to embed; 2) adaptiveselection of number of bits per pixel to embed; and 3) adaptiveselection of manner in which the information is inserted. Through theapplication of sensitive median-based statistical estimation and arecorded account of actions taken, the algorithms may be able to providethe desired level of security, both visually and statistically. Incomparison with other methods offering the same level of security, thenew technique is able to offer a greater embedding capacity. Inaddition, for the sake of thorough investigation and fair comparison, anew stego capacity measure which will offer a means of comparingsteganography methods applied across different formats of images.Finally, this new algorithm is created with the intention ofimplementing within the offered capabilities of a wireless mobiledevice.

In one respect, a new adaptive embedding technique that has been provento minimize the detection of steganographic media within digital imagesis provided. Through the application of sensitive median-basedstatistics, the algorithms may be able to provide a sound level ofsecurity while simultaneously increasing image capacity. Analysis isassisted by the use of a new stego capacity measure which offers insightto the relationship between capacity and image modification. Throughthis measure, the best candidate may be chosen to cover from a databaseof images with varying sizes, formats, and features.

Information to be embedded may be either done by changing coefficientsin the transform domain or, in one embodiment, remaining in the spatialdomain and altering the least significant bits of the binaryrepresentation of a digital image. The proposed LSB embedding algorithmstake advantage of the uncertainty and noise like properties that areassociated with certain regions within a candidate cover image. All dataacquisition devices, such as digital cameras, incur some level of noise.The changing frequencies of the color spectrum over an image alsoimitate noise like qualities. The ability to successfully identifyregions of an image where this idea is prominent may ensure the failureof detection software attacks. The algorithms for this new adaptiveapproach are derived from noise identification algorithms proposed byAgaian, Sifuentes, and Lamoroueux. These algorithms use detectionmeasures with increased statistical sensitivity to successfully separatenoisy pixels from those naturally associated with the image. Detailedcharacterization of the surroundings of a pixel give an idea of the bestapproach, and the amount of information that can be safely embedded at agiven pixel location. Additionally, the second stage of the embeddingprocess applies an equal amount of evaluation in working to counteractthe revealing statistical artifacts that are introduced from theinsertion of stego information. The basis structure of the algorithm isrepresented by the block diagram in FIG. 17. The presented method may besubdivided into two systems.

Again, the capability of applying fast algorithms and using fixed memorypartitions in order to develop a new system for the implementation ofsteganographic programs is provided. Though steganography istraditionally accomplished using the high processing speeds of a desktopor even notebook computer, recently technology has miniaturized highcomplexity circuitry giving rise to the convenience of portable andcomplex processing components. Such advancements are clearlydemonstrated in the field of wireless communications. Mobile devices arecontinuously increasing in the level and quality of services andapplications that are offered. With the introduction of mobile platformoperating systems, there arises the opportunity to create and developinnovative translations of commonly used processes.

The algorithm includes several stages producing an elaborate embeddingprocess. System I may be used for the development of variation measuresand explanation of the threshold derivation process including selectionof best locations to embed and/or selection of number of bits per pixelto embed. System II may execute a histogram retention algorithm andembedding process. The algorithm may also include a First-Order StegoCapacity Measure (FOSCM), computer simulations, and/or analysis.Analysis may be done using a database of 100 color and 100 grayscalebitmap images varying in size, color, and classes of image features.Stego files of different size and format may be also used to demonstratetheir affects on the detection process. In comparison to the readilyavailable embedding software as well as other existing adaptiveembedding methods, the present disclosure's method is shown to performquite well in maintaining a resistance to detection as well as enablingan increase in the amount of stego capacity. Detection tests were doneusing implementations of RS Steganalysis and Histogram Quick Frequency.

System I—Variation Measures and Threshold Derivation Process

The primary function of the variation measures may be to form afoundation for the adaptive selection of embedding locations and thenumber of bits per pixel to embed. This measure may be used as anintegral component to the noise detection algorithms. Fundamentally, theobjective is to modify the cover data to reflect the binary informationto be hidden while ensuring that changes do not exceed the noisethreshold. Algorithms intended for the detection of noise corruptedpixels may simply tuned down to the level of detecting natural imagenoise. FIGS. 18A (image corrupted with Gaussian Noise) and FIG. 18B(clean image) demonstrates the detection of noise both in an imagecorrupted with Gaussian noise and a clean image.

Pixels considered noisy are represented in gray and edge pixels may bedesignated in black. Edge pixels also have noise-like characteristicsand as a result, may be included for considerations of noise. The cleanimage with corrupted pixels identified demonstrates the presence ofnatural noise.

Median-Based Variance as a Variation Measure

The measure we use as a characterization of the variation within thevicinity of a pixel is a modified variance. Instead of using the mean ofthe acknowledge intensities, we substitute the median value. Theassociated equation is given below.

$\begin{matrix}{{{\eta\left( {i,j} \right)} = {\frac{1}{N}{\sum\limits_{n = 1}^{N}\;\left( {x_{n} - {{median}\left\{ x \right\}}} \right)^{2}}}},} & {{Eq}.\mspace{14mu} 6}\end{matrix}$where η is the variable assigned to the calculated local median-basedvariance. The location of the pixel of interest within the image isdesignated by i and j. The set of pixels within the window is x and N isthe total number of pixels within the window. The window size used inthis application is a 3×3, where the center value is the pixel for whichthe variation measure is calculated. A small window may be preferred inorder to maintain the absolute locality of the variation measure. In oneembodiment, it may not be rational to consider a pixel as being within ahigh variation region when the location of such is actually three pixelsaway from an area truly containing a sufficient level of noise.Antonymous is the minimum restriction of the window size. In order toaccount for variations in all directions, a specified window size may beused. A 2×2 window would only consider four directions compared to theeight directions included in a window of size 3×3. Additionally, thevariation measure of a 2×2 sample space would pertain to a pixel whoselocation was displaced by half a pixel both horizontally and vertically.

Selecting Best Locations to Embed Information and Adaptive Multi-BitEmbedding

The determination of the variation measure pertaining to each pixelenables the algorithm to select at which locations would be best toinsert information. If the local measures exceed a certain thresholdlevel, the pixel may be deemed as a suitable candidate for the insertionof binary data. If the variation measure is found to exceed thethreshold by a determined magnitude, the pixel may facilitate the hidingof multiple bits. This adaptive selection of how many bits per pixel toembed is the foundation of the increased capacity the proposed algorithmis able to offer. Under certain conditions, the noise level at adesignated region may be sufficiently high to allow a greater level ofmodification to the value of a pixel. FIG. 11 demonstrates the generalconcept behind this process.

Aside from the benefit of an increase in cover image capacity, the ideaof embedding multiple bits per pixel also assists in the retention offirst-order statistics. Since fewer pixels are modified, there may be areduced number in modifications of frequencies in the histograms.Furthermore, when embedding multiple bits, often the pixel may bereassigned a value other than the adjacent intensity. Necessaryadjustments are diffused at a further distance, counteracting the randomspikes that often occur within the histogram with various othertechniques. Thus, the increased embedding capacity property is alsoinherent in the decrease in probability of detection of steganographicactivity.

For a given example, a 512×512 grayscale image may be used as the covermedia. Stego information was hidden in approximately 75% of the pixelsin the cover image. A high percentage of embedded information may beinserted to make the random spikes more visually prominent. Thestatistical analysis is much more sensitive to histogram frequencycharacteristics than what is able of the human visual system.

$\begin{matrix}{{RMSE} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}\;{{{{x(i)} - {y(i)}}}^{2}.}}}} & {{Eq}.\mspace{14mu} 7}\end{matrix}$There are benefits to be realized through the application ofmultiple-bit embedding, but there is also a balance which must beconsidered. Overuse of the multi-bit embedding concept may dramaticallyincrease the amount of root-mean-square error (RMSE) introduced into theimage during the embedding process. A reduced number of pixels may bealtered, but a multitude of pixels may altered by a value greater thanone. RMSE may be derived from Eq. 7.

Adaptive Threshold Derivation Process

The purpose of the threshold derivation process may be to ensure an evendistribution of stego information throughout the image. This processselects the threshold in a manner in which the safest pixels may be usedfirst in relevance to the size of the stego file to be embedded. Tofacilitate extraction, information may be sequentially embedded fromleft to right, top to bottom. If the threshold is randomly selected, onemay be unsure of the resultant distribution of steganographic data. Avery high threshold may not accommodate the amount of space required.Setting the threshold excessively low may result in a higher thannecessary density of modified pixels. FIG. 19A (excessively lowthreshold) and FIG. 19B (adaptive threshold selection) provide anexample demonstrating the importance of selecting the proper threshold.Stego containing pixels are represented in black.

The decision of the threshold may be dependant of two variables: theamount of data that is to be inserted, and the magnitude of thedifference in thresholds distinguishing single and multi-bit carryingpixels. The relationship between the number of candidate pixels and thethreshold value is an exponential function as is displayed in FIGS. 20Aand 20B.

The Z axis of the left graphic in FIG. 20A represents the number ofpixels that qualify for stego insertion based on a particular thresholdvalue. The Y axis in the left graphic represents the ratio between thesingle-bit and multi-bit thresholds. The greater the value of thisratio, the sharper the knee in regards to the exponential function. Theimportance of the incline may be its determination in the level ofcontrol over the number of available pixels. Little control results in areduced ability to find an optimal distribution for stego information.Additionally, a higher ratio may reduce the allowable image capacity anddegrade the effectiveness of the multi-bit principle. However, there isan indirect relationship between the ratio and the RMSE value. A higherratio corresponds to a lower resultant error value. A low ratio resultsin a smooth and higher curve offering greater control of stegodistribution and an increased embedding capacity. The associatednegative factor may have an increase in the RMSE value, somewhatcontributing to the risk of detection. The decisive factor in theselection for the optimum balance may be the effects of the selectedparameters on first-order statistics and a measure for making suchdecisions is presented in a following section. Associated, histogramretention may be a significant factor crucial to the performance of theproposed algorithm.

System II—Histogram Retention Algorithm and Embedding Process

The selection of the threshold and applied ratio predetermines everylocation for the insertion of stego information. All the functions ofSystem I may be a required preliminary for the second system. Thepurpose of histogram retention may be an effort to preserve first-orderstatistics. Natural digital images may generally produce a smoothhistogram, characterized by gradual fluctuations throughout the entireintensity spectrum. As displayed in the previous example presented,steganographic processing has the effect of disrupting this expectedbehavior. The goal of histogram retention is to minimize this affect andmaintain the natural appearance of statistical properties of the image.In order to accomplish this, we create a vector that records all appliedpixel modifications. If the opportunity arises to counteract a previousmodification, the algorithm may recognize such and enact therectification. The histogram retention efforts may occur simultaneouslywith the embedding process. The data vector may be iteratively queriedand updated with each transformed pixel. The general procedure of thisprocess is accomplished by applying the following steps to the histogramretention algorithm.

In one embodiment, values of next bit\bits to be embedded may beinputted. Next, an identification step identifying associated bit\bitswith current qualified pixel, and if the comparison shows that qualifiedpixel and bit\bits are identical, there are no further actions to betaken. If the comparison shows that they are not identical, the twonearest pixel values with the required bit/bits may be found. A querydata may be used for recording the values of the lower associated numberand a vector may be updated by increment number for the selected pixelvalue. The original value of the converted pixel may be updated byincrementing an update vector. Next, the original value of the convertedpixel may be decreased.

First-Order Stego Capacity Measure (FOSCM)

The First-Order Stego Capacity Measure enables an optimal decision inthe balance between RMSE and first-order statistics. With this measure,\the best cover image for a specific stego file among a database ofimages varying in size, format, and features may be found.Simultaneously, different existing embedding algorithms to determine theoptimal performer may be tested. In order to calculate the FOSCM, ahistogram must first be created for the stego image and the originalcover image using, for example, Eq. 4. Upon derivation of the H value,the FOSCM may be calculated using, for example, Eq. 5. With thecalculation of Ω comes the ability to simultaneously minimize RMSE andaltering of first-order statistics.

Computer Simulation

Computer simulations were simulated in the MATLAB software package.Initial tests were intended to analyze the histogram differencesintroduced by three different adaptive embedding algorithms. The firstmethod is from the referenced article by Fridrich and Rui. This methodmay use a normal variance to determine variation measures associatedwith each pixel. For fair comparison, an optimal threshold derivationfor an even distribution of steganographic information may be used.

The second method includes an adaptive, multi-bit capability methodwithout histogram retention processing and the third is our fullyadaptive algorithm with histogram retention. The test images used inthis analysis are the 512×512 grayscale images displayed in FIG. 21A(fisherman sign) and FIG. 21B (Trolley).

Incremental sizes of stego files may be embedded using each method andthe associated H values are plotted in FIG. 22. The X axis in of thefigure represents the ratio of number of bits resultant of the binaryconversion of the stego data to the number of pixels in the cover image.Percentages from 10% to 80% in increments of 10% were introduced intothe cover image. The Y axis of FIG. 22 represents the sum of themagnitude differences between histograms of the original and stegoimages. The resultant values for each percentage of embedding arerepresented by three distinguishably formatted plots. The proposedmethod proves to give the lowest level of histogram modification incomparison to the other adaptive methods. The random embedding methodSecurEngine, was excluded from this simulation to reduce graphicalclutter but will be acknowledged in the following example to demonstrateits associated deficiencies in comparison to the adaptive methods.

The second example shows the magnitude of the difference for eachparticular intensity frequency in relation to the original and stegoimages. The number of bits embedded is 10% of the number of pixels inthe cover image. These plots are from steganography embedded into thefishermen test image (FIG. 21A). In FIG. 23A, a proposed histogram isshown. In FIG. 23B is an adaptive method from current technologies. InFIG. 23C is a proposed method without histogram retention. In FIG. 23Dis a histrogram from SecurEngine.

In the following histograms, the amount of stego information with up to80% of the number of pixels in the cover image is tested. The resultantplots are presented in FIG. 24A, a proposed histogram is shown, in FIG.24B is an adaptive method from current technologies, in FIG. 24C is aproposed method without histogram retention, and in FIG. 24D is ahistrogram from SecurEngine.

In combination with the example above, the histograms of the stego imageusing the proposed method and the stego image using the simple adaptivealgorithm by Fridrich and Rui are compared to the histogram of theoriginal image in FIGS. 25A (histogram of a cover image) and 25B(histogram of stego image with best threshold). These histograms,excluding the original cover image, may be embedded with an amount ofbits approximately equivalent to 80% of the number of pixels in thecover image.

The final example expresses the extent to which we may embed informationand not noticeably alter the ideal histogram. In summary, there is anassociated capacity with this permitted boundary. This boundary may berelevant to the region preceding the knee of the exponential functionshowing the relationship between the threshold and the number ofqualified pixels associated with that particular threshold. The optimumsecure capacity may be attained by constraining the threshold in theregion directly prior to the point in which control of distributionbecomes unstable. In relation to the curve on the left of FIG. 14, thisregion would lie in the threshold range from approximately 50-60. FIG.26A (original image histogram), 26B (histogram of image after methods ofthe present disclosure), and 26C (histogram using referenced adaptivemethod) show the histogram when the threshold is assigned within thisregion.

Through the use of the FOSCM, the best image for embedding a particularstego file may be selected. FIG. 27 shows the calculated FOSCM valuesassociated with embedding the file into the five test images. The imagepertaining to the lowest FOSCM measure is the best image to use as thecover for the specified stego. For this particular test stego file, thebest cover image was determined to be the chess match image (4^(th)column).

To further test the security associated of the algorithm,implementations of RS steganalysis and Histogram Quick Frequency wastested. Table 7 gives the detection results when applying RSsteganalysis. RS steganalysis is well known to detect even very smallstego messages. Given is the detected message length in terms of bytes.10% pertains to a message length of 3,249. 33% pertains to a messagelength of 10,816 and 50% pertains to a message length of 16,384.

TABLE 7 RS steganalysis attack - Estimated Length of Message in Numberof Bytes SecurEngine Adaptive [5] Non-Adap. [5] New Method Fish 10% 1591,782 5008 379 Fish 33% 793 11,707 12,022 624 Fish 50% 1824 17,10517,596 730 Gold 10% 4,419 7,125 5,129 76 Gold 33% 16,905 7,125 12,876185 Gold 50% 15,939 10,027 18,831 361 Rock 10% 9,947 2,585 5,209 805Rock 33% 15,810 12,800 12,675 934 Rock 50% 11,782 18,472 17,935 1197Sarkis 10% 1,655 808 3,346 −288 Sarkis 33% 2,845 10,378 10,692 78 Sarkis50% 3,072 15,248 16,724 639 Trolley 10% 12,060 1,676 4,641 219 Trolley33% 16,821 9,519 12,475 711 Trolley 50% 1,631 13,949 17,394 1035

TABLE 8 Histogram Quick Frequency Attack SecurEngine Adaptive [5]Non-Adap. [5] New Method Fish 10% N N N N Fish 33% Y Y N Y Fish 50% Y YY Y Gold 10% N N N N Gold 33% Y N N N Gold 50% Y Y Y N Rock 10% N N N NRock 33% N N Y N Rock 50% Y Y Y N Sarkis 10% Y Y Y N Sarkis 33% Y N Y NSarkis 50% Y N N N Trolley 10% Y Y N N Trolley 33% N N Y N Trolley 50% NY Y NThe above tables were tested using 8-bit grayscale images with dimension512×512. The RS Steganalysis generally failed to detect the level ofinformation that was actually embedded but succeeded in detecting foralmost all other methods of steganography. Similarly, histogram quickfrequency detected stego in the new method in the fewest number oftrials. Outperformed were SecurEngine, an adaptive method and a randommethod.

We have presented an adaptive method of steganography that successfullyreduces the probability of detection while simultaneously allowing ahigher image capacity when compared to other adaptive embeddingtechniques such as the algorithms presented by Fridrich and Rui as wellas existing random embedding methods. Using the adaptive multi-bitprinciple, more information can be inserted which has the additionalbenefit of reducing the necessary changes of first-order statistics as aresult of utilizing a reduced number of cover image pixels.Additionally, the adaptive assignment of the threshold in order to allowfor an ideal distribution of stego information throughout the imagefurther assists in the decreasing the risk of detection. Finally, byincorporating a concept presented by Franz, a first-order statisticsthwarting any histogram based steganalysis attacks were not changed.Figures showing the histograms of outputted images demonstrate thereduction in identifiable artifacts commonly introduced by the embeddingprocess. As an added feature, we have introduced the First-Order StegoCapacity Measure (FOSCM) which optimizes the balance between minimizingRMSE and disturbances of first-order statistics. Finally, thesealgorithms have been formulated in a manner that will accommodate theimplementation of such a system within the capabilities of thetechnology offered by the latest wireless mobile devices. These ideasare currently being further developed to facilitate the transition ofcreated algorithms into the new environment.

Palette-Based Steganography Used for Secure Digital Image Archiving

This section focuses on essential problems faced in digital imagearchiving. One of the principal objectives is to preserve secure digitalmedia and information for the future. In addition to identifying fileformats where storage systems and current technology are still in theirinfancy research is the key component in maturing these systems.Furthermore, with developing new generation of archiving system, thereis a need to have a high capacity embedding methods. The presentdisclosure stores digital image content within a secure storagepreserving the content without allowing an unauthorized third party theability to view the content even in the event that the security isbreached. A new secure high capacity embedding method may be used in asecure multilayer database system for digital image archiving. Thissteganographic approach has the following advantages: 1) BPCSSteganographic method for palette-based images; 2) provides additionalsecurity through a simple selective color and cover image algorithm; 3)provides the closest color substitutes using a weighted distancemeasure; 4) offers an increased capacity by embedding in N×M blockssizes; and 5) the secure media storage system contains an independentsteganographic method that provides an additional level of security.

There are several algorithms that are used to embed information in coverimages. These algorithms use inconspicuous looking carriers that takeadvantage of the gaps in the human visual and audio systems to hideinformation. Digital images contain considerable amounts of redundantdata; therefore they are ideal carriers used to conceal the presence ofsecret information. There are many color models used to representdigital images and all of them can be modified to embed information. Themost popular color models used for this purposes are the 24-bit RGB or“true-color format” and palette-based color format. True-color imagesare composed of three layers, corresponding to the primary colors: Red,Blue, and Green. Each color layer is represented using 8-bits fortrue-color images. Palette based images on the other hand, arerepresented using an index and a palette. The palette contains all theimage's colors while the index contains only addresses that point to aspecific location in the palette.

Problems with existing palette steganographic methods are that theinformation is limited and the hidden message can be destroyed byswitching the order of the palettes. In this disclosure, a palette-basedsteganographic method with increased security and embedding capacity isprovided.

Another area of interest is the capacity that digital media can handlebefore visual and statistical artifacts cause degradation to the coverfile. Many steganographic capacitates have been proposed to improve bothsteganographic embedding and detection processes within media files.Several authors consider capacity from the communications point of view,or consider the capacity as the value of a mutual-information gamebetween the data hider and the attacker, for example 1) data hidingcapacity has been calculated as the capacity of the Gaussian channel; 2)the capacity of the data hiding channel has been regarded as intentionaljamming for the source of noise; 3) a comprehensive model is described,which analyzes the performance of a proposed scheme for data hiding; 4)estimates data-hiding capacity has been estimated as a maximum rate ofreliable transmission, for host-image sources; 5) a theoretic approachhas been presented which obtains an estimate of the number of bits thatcan be hidden in still images, or the capacity of the data-hidingchannel; and 6) estimated boundaries have been calculated forsteganographic-communication channel capacity.

The method of the present disclosure may be applied to a securemultilayer database system for digital image archiving. This disclosurediscusses the necessary background for bit plane complexity measure andthe bit plane stego sensitivity measure used for image capacity. Palettebased bit plane steganography is described in the third section followedby the computer simulation.

Bit Plane Complexity Measure

This section will introduce some necessary definition needed for thederivation of the stego sensitivity measure and investigation of somemeasurement properties.

Masks:

${{Let}\mspace{14mu} M_{R,C}} = \begin{bmatrix}\bullet & \ldots & \bullet \\\vdots & \ddots & \vdots \\\bullet & \ldots & \bullet\end{bmatrix}$be any mask at R, C pixel location. The number of adjacent pixelssurrounding a center pixel at the pixel location may be denoted R, C.The binary pixels compared |P_(R,C)−P_(R-1,C)|<1, defined by the maskused and block size. Unlike the complexity measure defined for a given8-bit color layer, the pixels for each bit plane decomposition may havea difference of less than one. β is an incremental count of the adjacentpixel comparisons that meets the given threshold of one, and β_(max) themaximum number if all adjacent pixel comparisons meet the thresholdwithin a block size and a moving mask. The same pixels may not to becompared. Other masks as defined in the original complexity measure maybe used as well.

Bit Plane Stego Sensitivity Measure

Let I be an image of size N₁×N₂ and let I_(B) be the bit planedecomposition of an 8-bit color layer. Each of the decomposed bit planesmay be divided into blocks, sub bit planes, of M₁×M₂ from the imageI_(B). Then the pixel comparison based complexity measure for bit planeis defined as:

$\begin{matrix}{{{\gamma_{i}\left( {m,n} \right)} = \frac{\beta}{\beta_{\max}}},\mspace{14mu}{i = 0},1,\ldots\mspace{14mu},7,} & {{Eq}.\mspace{14mu} 8}\end{matrix}$where m and n are the block locations being analyzed and i defines thebit plane layer. Each of the bit planes are scanned individually forideal regions of embedding.

Stego Sensitivity Measure for an individual bit plane is defined by:

$\begin{matrix}{{\Gamma_{i} = {\frac{1}{MN}{\sum\limits_{m = 1}^{M}\;{\sum\limits_{n = 1}^{N}\;{\gamma_{i}\left( {m,n} \right)}}}}},\mspace{14mu}{i = 0},1,\ldots\mspace{14mu},7,} & {{Eq}.\mspace{14mu} 9}\end{matrix}$where γ_(i)(m, n) are the block values containing all of the complexityvalues within the image bit plane, N is the number of rows in Γ and M isthe number of columns in Γ. Non-zero values are not stored to derive atrue threshold for the bit plane.

The general algorithm for calculation of bit plane stego sensitivitymeasure includes inputting an image of any size to be analyzed forembeddable regions. The image may be divided into color layers followedby a bit plane decomposition step. Next, a block size is determined andthe bit plane is divided into analyzed sections. A mask size may bedecided. The value of γ for each block may be calculated ensuring theblocks overlap. An initial value for the threshold of Γ may becalculated. Next, γ is determined to see if its value meets thethreshold and categorize γ is into embeddable regions. Rows and columnlocation of the embeddable region may be outputted.

The steganographic capacity is used to separate the image intoembeddable regions within the image and non-embeddable areas. This isshown in HG. 28 where the highlighted blocks may be consideredembeddable areas and the natural spaces are non-embeddable regions.These designations will be used to embed secure information within theembeddable regions.

Palette Based Steganography

In this section, a necessary background for palette based images usedfor embedding digital content is presented. The capacity measurenecessary for embedding is also described in this section.

Due to high redundancy within the image, true-color images have higherembedding capacities. However, palette-based images have been used ascover images to provide a secure and fast transmission/storage over acommunication system. Due to their abundance within vast systems, it isdifficult to find a suspicious stego-image. Furthermore, since there arealterations introduced by the color reduction and errors in storage, thesteganographic message is able to pass as noise. Given thatpalette-based images have a smaller resolution, they can also betransmitted faster than 24-bit resolution images through a communicationchannel during archiving.

In order to solve the flaws of existing methods, a new capacitypalette-based steganographic approach is presented. Two new practicalsteganographic including embeddable and non-embeddable capacities areprovided. These methods randomly select pixels in the index of anembeddable region within an image and arrange it (using special sortingscheme) in such an order to provide the desired secret key. This methodmay replace a randomly selected pixel by the closest pixel in the colorspace that contains the desired parity bit. In addition, the newcapacities palette based embedding may be used for 1) separation of agiven image into embeddable and non-embeddable areas; 2) identificationof the minimum and maximum embedding capacities of a cover digitalimage; 3) selecting the best cover image from a class of images; and 4)embedding only in selected colors of the palette. This offers theadvantage that secures data can be embedded within the index, thepalette, or both therefore, adding an extra level of security. Thismethod embeds in the palette by applying sensitivity measure techniquepreviously used for gray scale and color images. This technique isapplied to full color images by decomposing the image into its threecolor layer components within the digital image and treating each layeras a gray scale image. Next, an embedding into one or more of the colorcomponents of each layer of the images may be performed. However, evenif only one of the color component images is used for embedding, thenumber of colors in the palette after embedding may be over the maximumnumber allowed. In order to represent the image data using a properpalette-based format, color quantization may be applied. Afterinformation is embedded, it may be imperative that pixel values of thecolor component contains the embedded information, otherwise the securedata can be lost. Changes may be made to the pixel values of the othertwo color components within the images. It is assumed that the degradingof the color component image with information embedded is smaller thanthat of the color component images that are used for color reduction.The human visual system is more sensitive to changes in the luminance ofa color. Since G has the largest contribution to luminance of the threecolor components, information is primarily embedded in this layer. Oneof the effects caused by this method is that the number of colors in thepalette changes. In order to balance the palette to the original numberof colors, the R and B components may be modified in a way thatminimizes the square error.

Bit Plane Palette Based Stego Sensitivity Measure

This section describes basic methodology for applying the Bit PlaneStego Sensitivity Measure to palette-based images. In addition, weintroduce a new improved steganographic method. Finally, some the mostcommon flaw of these algorithms are discussed.

Recently, methods that embed in 24-bit images have been applied topalette-based 8-bit images. This is achieved by changing color models,for example by going from palette-based to RGB method, embedding andgoing back to the palette-based model. These methods exploit the factthat alterations may be introduced by the color quantization procedureduring the transition of one color model to then next. Embedding maytake place in these alteration paths or noisy areas. In this approach, aBPSC approach may be used to separate the noisy patterns frominformational patterns. The idea is to embed only in noisy patternsleaving the informative areas intact, therefore introducing less noisewhile embedding. In addition images that embed using BPCS approach areharder to detect. In order to further enhance the quality of the stegoimage, the information may be embedded in a way that the color that isthe most sensitive to changes is not affected. This algorithm however,can not be applied directly to a particular color in the palette-basedimage. Since the color vectors in a palette are form as a combination ofthe red, green and blue, the image has to be separated in to its colorlayers using the RGB color mode. After embedding we most go back topalette-based, this is achieved using color quantization.

The next step in this algorithm is to determine which color is the mostsensitive to changes. In some respects, the color green was chosen sinceit has the most effect on the luminance and the human eye is moresensitive to changes in the luminance. The reason for selecting the mostsensitive color layer for embedding is to preserve the luminanceinformation. The information may subsequently be embedded in the noisyareas selected by the binary pattern complexity system.

In other embodiments, a new palette-based steganographic method withimproved selective quality and embedding capacity is provided. In thisalgorithm, a Bit Plane Stego Sensitivity Measure (BPSSM) may be appliedto one of the color components that resulted from switching to the RGBmodel. Next a layer for embedding is selected. It is noted that the bestlayer can vary depending on the image used. Using the BPSSM, embeddableregions from the selected layer may be extracted. Note, this algorithmmay only embed on the selected areas of the best color layer.

The general algorithm for palette based steganography using complexitymeasures includes an input step for inputting an image of any size to beanalyzed. Color values from the image may be obtained and a color layerfor embedding may be selected. Embeddable regions using the bit planestego sensitivity measure may be obtained and may be applied to theselected color layer. A new palette using the best color layer may beproduce, while the other layers of the image remain intact. The embeddedpalette-based image may be outputted.

In most cases, after embedding, the number of color vectors exceeds 256(0-255) colors. This may be a problem since the maximum color intensityin an 8-bit palette representation is 255. The usage color quantizationmay guarantee that the new palette will not exceed 256 colors. Thisprocess may be achieved without changing the selected color layer, sincethis is were the secret information lies. In order to begin the colorreduction procedure, color vectors that share the same value of theselected color layer may be obtained to make a new table. Using thistable, the new color vectors for red and blue may be computed. Forexample, if selected color layer is a green layer, three red and bluecolors that share a green may be obtained and a mean value may becalculated. These values are denoted mean read (MR) and mean blue (MB).For improvement in the reduction method, the square errors to determinewhich combinations are the best are calculated.

Computer Simulations

In this section, the algorithm of the present disclosure is comparedwith the BPCS palette based algorithm. Tables 9 and 10 illustrate thedifferences by incrementing the embedded data. In FIG. 30, the maximumamount of data that it takes to load the image with secure data withoutcausing visual changes was embedded. The same amount of information wasembedded using BPCS method the embedded data causes statistical andvisual changes within the image. The presented method's maximum capacitywhile embedding in the lower bit plane layers fluctuated between 9.4kand 13.6k while the BPCS method fluctuated between 5.4k and 8.7k.

TABLE 9 This table contains the RMS values obtained by comparing theclean and stego Sarkis images Image-RMS M Presented Method BPCS Method4k 0.233 0.242 5k 0.284 6.757 6k 0.325 7.255 7k 3.013 7.260 8k 4.8498.781 13k  8.961 10.171

TABLE 10 This table contains RMS values obtained by comparing the cleanand stego Golden Gate images. Image-RMS M Presented BPCS 4k 0.244 0.2495k 0.288 9.685 6k 5.180 10.061 7k 8.019 11.842 8k 10.017 13.366 13k 14.001 15.155

In this section, the presented algorithm (using both RGB and YCbCr colorformats) was compared with the BPCS palette based algorithm by embeddingin 256×256 images. Tables 11 and 12 illustrate the differences byincrementing the embedded data. In FIG. 31, a maximum amount of datathat it takes to load the image with secure data without causing visualchanges was embedded. The same amount of information using BPCS methodwas embedded and the embedded data causes more statistical and visualchanges within the image than both presented methods (RGB and YCbCr).The presented method's maximum capacity while embedding in the lower bitplane layers fluctuated between 9.4k and 13.6k while the BPCS methodfluctuated between 5.4k and 8.7k. The presented method for both RGB andYCbCr color models is better at identifying the noisy regions. The bitplane stego sensitivity measure adaptively selects the best embeddableblock within the noisy regions. This is the reason why the presentedmethod has outperformed the BPCS palette based steganography scheme.

TABLE 11 This table contains the RMS values obtained by comparing theclean and stego boy images. Presented Presented BPCS Size RGB YCbCr RGB4k 0.233 0.246 0.242 5k 0.284 0.287 6.746 6k 0.325 0.331 7.339 7k 3.0511.885 7.404 8k 4.900 2.545 8.954 13k  9.109 4.330 10.250 

TABLE 12 This table contains the RMS values obtained by comparing theclean and stego boy images. Presented Presented BPCS Size RGB YCbCr RGB4k 0.233 0.246 0.242 5k 0.284 0.287 6.746 6k 0.325 0.331 7.339 7k 3.0511.885 7.404 8k 4.900 2.545 8.954 13k  9.109 4.330 10.250

The above section presented a new secure high capacity embedding methodused with a secure multilayer database system for digital imagearchiving. This steganographic approach has shown the followingadvantages, including bit plane stego sensitivity measure forpalette-based images provides a secure and fast transmission/storageover a communication system, offers additional security through a simpleselective color and cover image algorithm, provides the closest colorsubstitutes using a weighted distance measure, offers an increasedcapacity by embedding in N×M adaptive blocks sizes, the secure mediastorage system contains an independent steganographic method thatprovides an additional level of security, among other advantages. Thismethod may be used to further enhance the steganographic algorithmsutilized for digital image archiving purposes.

Security Considerations

In this section, security issues will be addressed using well knowsteganalysis techniques. The security of the presented methods wastested using 100 hundred palette-based images using the followingmethods: Chi-Square, Pairs Analysis, and RS Steganography. These imageswhere embedded using the RGB and YCbCr color models. The analysis showsthat the presented method is completely immune to the Chi Square andPairs Analysis steganalysis methods. However, RS Steganalysis detectedhidden information within the green layer of cover images that wereembedded using the RGB color model (see Table 15). To improve thesecurity of the presented method information was hidden using theluminance layer of the YCbCr color model to embed (see Table 16).

TABLE 13 This table contains the averages of the detection percentageresults of 100 512 × 512 palette based images using Chi SquareSteganalysis. 0% 1% 3% 6% 12% 23% 40% 76% 95% Average 0.01% 0.04% 0.13%0.26% 0.53% 1.03% 1.82% 3.57% 4.41% Stego Detected

TABLE 14 This table contains the averages of the detection percentageresults of 100 512 × 512 palette based images using Pairs Analysis. 0%1% 3% 6% 12% 23% 40% 76% 95% Average 1.85% 3.11% 2.96% 5.17% 5.38% 4.24%4.02% 7.66% 9.60% Stego Detected

TABLE 15 This table contains the averages of the detection percentageresults of 100 512 × 512 palette based images using RS Steganalysis.This images where embedded in the RGB color model. Detection CoverEmbedded Detection Clean Image Differences % Red Green Blue Red GreenBlue Red Green Blue  1% 2.93% 2.68% 2.74% 2.93%  2.94% 2.74% 0.00% 0.26% 0.00%  3% 2.93% 2.68% 2.74% 2.93%  3.41% 2.74% 0.00%  0.74% 0.00% 6% 2.93% 2.68% 2.74% 2.93%  4.43% 2.74% 0.00%  1.75% 0.00% 12% 2.93%2.68% 2.74% 2.93%  6.39% 2.74% 0.00%  3.72% 0.00% 23% 2.93% 2.68% 2.74%2.93% 11.25% 2.74% 0.00%  8.57% 0.00% 40% 2.93% 2.68% 2.74% 2.93% 18.59%2.74% 0.00% 15.92% 0.00% 76% 2.93% 2.68% 2.74% 4.08% 36.70% 2.87% 1.15%34.03% 0.13% 95% 2.93% 2.68% 2.74% 3.41% 42.51% 3.04% 0.49% 39.83% 0.30%

TABLE 16 This table contains the averages of the detection percentageresults of 100 512 × 512 palette based images using RS Steganalysis.This images where embedded in the YCbCr color model. Detection CoverEmbedded Detection Clean Image Differences % Red Green Blue Red GreenBlue Red Green Blue  1% 2.93% 2.68% 2.74% 4.00% 3.58% 4.95% 1.08% 0.91%2.21%  3% 2.93% 2.68% 2.74% 4.03% 3.53% 4.91% 1.10% 0.85% 2.18%  6%2.93% 2.68% 2.74% 4.05% 3.41% 4.77% 1.13% 0.74% 2.03% 12% 2.93% 2.68%2.74% 3.95% 3.24% 4.59% 1.03% 0.57% 1.85% 23% 2.93% 2.68% 2.74% 3.98%3.24% 4.34% 1.05% 0.57% 1.60% 40% 2.93% 2.68% 2.74% 3.60% 3.03% 4.18%0.67% 0.35% 1.44% 76% 2.93% 2.68% 2.74% 3.58% 3.74% 4.13% 0.66% 1.07%1.39% 95% 2.93% 2.68% 2.74% 4.09% 6.41% 3.79% 1.16% 3.74% 1.05%

In this disclosure, a new, secure, high capacity embedding methodcapable of embedding in multiple bit-planes using different color modelis provided. This steganographic approach has shown the followingadvantages: 1) bit plane stego sensitivity measure for palette-basedimages provides a secure and fast transmission/storage over acommunication system; 2) Offers additional security through a simpleselective color and cover image algorithm; 3) Offers an increasedcapacity by embedding in N×M adaptive blocks sizes; 4) Offers the optionof working with different color models; and 5) secure media storagesystem contains an independent steganographic method that provides anadditional level of security. This method can be used to further enhancethe steganographic algorithms utilized for secure digital imagetransmission.

The methods of the present disclosure may be performed using executableinstructions. For example, a computer code for implementing all or partsof this disclosure may be used. The code may be housed on any computercapable of reading such code as known in the art. For example, it may behoused on a computer file, a software package, a hard drive, a FLASHdevice, a USB device, a floppy disk, a tape, a CD-ROM, a DVD, ahole-punched card, an instrument, an ASIC, firmware, a “plug-in” forother software, web-based applications, RAM, ROM, etc. The computer codemay be executable on any processor, e.g., any computing device capableof executing instructions for traversing a media stream. In oneembodiment, the processor is a personal computer (e.g., a desktop orlaptop computer operated by a user). In another embodiment, processormay be a personal digital assistant (PDA) or other handheld computingdevices.

In some embodiments, the processor may be a networked device and mayconstitute a terminal device running software from a remote server,wired or wirelessly. Input from a source or other system components maybe gathered through one or more known techniques such as a keyboardand/or mouse. Output, if necessary, may be achieved through one or moreknown techniques such as an output file, printer, facsimile, e-mail,web-posting, or the like. Storage may be achieved internally and/orexternally and may include, for example, a hard drive, CD drive, DVDdrive, tape drive, floppy drive, network drive, flash, or the like. Theprocessor may use any type of monitor or screen known in the art, fordisplaying information. For example, a cathode ray tube (CRT) or liquidcrystal display (LCD) can be used. One or more display panels may alsoconstitute a display. In other embodiments, a traditional display maynot be required, and the processor may operate through appropriate voiceand/or key commands.

With the benefit of the present disclosure, those having ordinary skillin the art will comprehend that techniques claimed here may be modifiedand applied to a number of additional, different applications, achievingthe same or a similar result. The claims cover all such modificationsthat fall within the scope and spirit of this disclosure.

1. A method comprising: providing a cover image; converting the cover image into a binary bit stream; decomposing the binary bit stream into a plurality of segments; generating a binary sequence based on a first key; generating a code matrix based on all combinations within the binary sequence; determining a distance between the code matrix and each of the plurality of fixed length segments to develop a second key; combining the first key and second key to produce a compressed and encrypted image of the cover image.
 2. The method of claim 1, where converting the cover image comprises: decomposing the cover image into color layers; decomposing each color layers into a combination of various binary layers; and reformatting the various binary layers into a single binary vector.
 3. The method of claim 1, where generating a binary sequence comprises generating a binary sequence with M-sequence or a Gold codes.
 4. The method of claim 1, where generating a binary sequence comprises generating a binary sequence with low redundancy.
 5. The method of claim 1, where decomposing the bit stream comprises decomposing the binary bit stream into a plurality of fixed length segments or varying length segments.
 6. A method comprising: providing a cover image; converting the cover image into a binary bit steam; decomposing the binary bit stream into a plurality of segments; classifying each of the plurality of segments into classes using a redundancy of bits based on a first key to generate a plurality of redundant and non-redundant segments; compressing the redundant segments; compressing the non-redundant segments with low redundancy based on a second key; combining the compressed redundant segments and non-redundant segments to form a compressed image of the cover image.
 7. The method of claim 6, where compressing the non-redundant segments comprises: generating a code matrix based on all possible combinations within the binary bit stream; determining a distance between the code matrix and each of the plurality of segments to develop a third key; and combining the first, second, and third keys to form compressed and encrypted segments.
 8. The method of claim 6, further comprising transforming the plurality of segments based on a fourth key.
 9. The method of claim 6, further comprising error correcting encoding the determined distance and each of the plurality of segments to develop the third key. 